Jim Fan 团队开源 CaP-X，让智能体以机械臂和人形机器人形态在物理世界中运行，并可通过感知 API、执行 API 与自动合成的技能库完成任务。配套发布 CaP-Gym 的 187 个操作任务、覆盖 12 个前沿 LLM/VLM 的 CaP-Bench 八级评测，以及无需训练的 CaP-Agent0；CaP-RL 中 7B 开源模型经 50 次训练迭代成功率从 20% 升至 72%。
团队开源 CaP-X 智能体机器人栈，并给出 CaP-Gym、CaP-Bench 与 CaP-RL 的系统性实验，可观察 LLM 在物理任务上的能力边界。
The power of the Claw, in the palm of a robot hand. Agentic robotics is here! Today, we open-source CaP-X: vibe agents, alive in the physical world. They incarnate as robot arms and humanoids with a rich set of perception APIs, actuation APIs, and auto synthesize skill libraries as they go. CaP-X is a strict superset of our old stack, because policies like VLAs are “just” API calls as well. It solves many tasks zero-shot that a learned policy would struggle with.
And we are doing much more than vibing. CaP-X is our most systematic, scientific study on agentic robotics so far:
- We build a comprehensive agentic toolkit: perception (SAM3 segmentation, Molmo pointing, depth, point cloud), control (IK solvers, grasp planner, navigation), and visualization (EEF, mask overlays) that work across different robots.
- CaP-Gym: LLM’s first Physical Exam! 187 manipulation tasks across RoboSuite, LIBERO-PRO, and BEHAVIOR. Tabletop, bimanual, mobile manipulation. Sim and real. Can’t wait to see the gradients flow from CaP-Gym to the next wave of frontier LLM releases.
- CaP-Bench: we benchmark 12 frontier LLMs/VLMs (Gemini, GPT, Opus, Qwen, DeepSeek, Kimi, and more) across 8 evaluation tiers. We systematically vary API abstraction level, agentic harness, and visual grounding methods. Lots of insights in our paper.
- CaP-Agent0: a training-free agentic harness that matches or exceeds human expert code on 4 out of 7 tasks without task-specific tuning.
- CaP-RL: if you get a gym, you get RL ;). A 7B OSS model jumps from 20% to 72% success after only 50 training iterations. The synthesized programs transfer to real robots with minimal sim-to-real gap.
3 years ago, our team created Voyager, one of the earliest agentic AI that plays and learns in Minecraft continuously. Its key ideas — skill libraries, self-reflection loops, and in-context planning — have since influenced many modern agentic designs.
Today, the agent graduates from Minecraft and gets a real job. It’s April Fool’s, but this Claw is getting its hands dirty for real!
Link in thread:
来源：Jim Fan · x.com